Integrative analysis of multi-omics data for liquid biopsy

Area of Science:

• Oncology
• Biotechnology
• Bioinformatics

Background:

• Liquid biopsy offers revolutionary potential for cancer management via early diagnosis and treatment.
• Integrative analysis of multi-omics data from body fluids can surpass single-modality approaches for cancer detection.

Purpose of the Study:

• To review multi-omics liquid biopsy for early cancer detection and molecular residual disease surveillance using blood.
• To summarize tumor-derived components, profiling platforms, and machine learning for liquid biopsy data analysis.

Main Methods:

• Review of current literature on multi-omics liquid biopsy strategies.
• Discussion of tumor-derived components, profiling platforms, and machine learning algorithms.
• Analysis of applications in early cancer detection, MRD monitoring, and treatment response.

Main Results:

• Multi-omics liquid biopsy enables comprehensive profiling of cancer-associated signals in blood.
• Machine learning facilitates joint analysis of diverse omics data for improved accuracy.
• Applications include early cancer detection, molecular residual disease monitoring, and treatment surveillance.

Conclusions:

• Multi-omics liquid biopsy shows significant promise for improving cancer management.
• Advancements in experimental and computational methods are key to clinical implementation.
• Integration into clinical workflows can enhance decision-making and patient outcomes.